Instructions to use yinita/gigpo-sft-w3c20-sync1-0726-step30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yinita/gigpo-sft-w3c20-sync1-0726-step30 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/home/sti/models/ps4mas-sft-x5-single-ep3-vllm") model = PeftModel.from_pretrained(base_model, "yinita/gigpo-sft-w3c20-sync1-0726-step30") - Notebooks
- Google Colab
- Kaggle
gigpo-sft-w3c20-sync1-0726 โ step 30
LoRA adapter from GiGPO run gigpo_sft_w3c20_sync1_50_0726_0459 at step 30.
Run config
- Base:
yinita/ps4mas-sft-x5-single-ep3 - Judge: Claude Sonnet 4.6 (Bedrock)
- Reward: phase2-only (
acq_w=3,comp_w=20),reward_phase1_steps=0 sync-every=1,force_acquire_frac=0, max_steps=50- WandB: https://wandb.ai/yinita/PS4MAS_GiGPO/runs/cgtq25cv
Case study @ step 30 (n=10, Sonnet)
- mean_composite: 2.532
- acquire_rate: 0.20
Series
| repo | step | CS composite | CS acq |
|---|---|---|---|
yinita/gigpo-sft-w3c20-sync1-0726-step10 |
10 | 3.066 | 0.50 |
yinita/gigpo-sft-w3c20-sync1-0726-step20 |
20 | 2.732 | 0.40 |
yinita/gigpo-sft-w3c20-sync1-0726-step30 |
30 | 2.532 | 0.20 |
yinita/gigpo-sft-w3c20-sync1-0726-step40 |
40 | 2.600 | 0.10 |
yinita/gigpo-sft-w3c20-sync1-0726-step50 |
50 | 2.499 | 0.10 |
Best CS in this series: step10. Adapter-only (no optimizer). Load as LoRA on the SFT base (e.g. vLLM --enable-lora).
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